Hidden risk factor evaluation method and device for risk event, equipment and medium

Abstract

The invention discloses a hidden danger factor assessment method, device and equipment of a risk event and a medium. The hidden danger factor assessment method for the risk event comprises the steps of obtaining a prior function type of a current potential safety hazard in a target risk event; determining each prior external factor of the current potential safety hazard and a market proportion of each prior external factor, and based on a target prior combination determination rule and each prior external factor, creating a target prior combination; and based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the event correlation parameter preset value, the market proportion of each prior external factor and the prior function type, a hidden danger factor evaluation curve is drawn, and the evaluation result accuracy of risk event hidden danger factor evaluation and the analysis comprehensiveness can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety management of equipment and facilities, and particularly to a method, device, equipment and medium for evaluating hidden danger factors of risk events. Background Art

[0002] The safety management of equipment and facilities is a key link in industrial production, energy supply, operation of public facilities and other fields. Its main goal is to ensure the efficient and reliable operation of equipment and facilities. However, due to the complexity of modern facilities and various uncertain factors in the management process, the existing technologies have the following obvious defects in the quantitative analysis of risk events and the evaluation of the impact of hidden danger factors:

[0003] Limitations of fixed MTTF (Mean Time To Failure) assignment: For the influence of external factors (such as environmental temperature and humidity, pollution degree, etc.), a fixed MTTF value is usually used for description. This static and fixed assignment method cannot dynamically reflect the real-time influence of external environment changes on the operation life of equipment, and it is difficult to meet the requirements of precise risk management.

[0004] Generally, qualitative descriptions (such as "high, medium, low" or "good, average, poor") are used to grade hidden danger factors, lacking strict quantitative standards and scientific assignment methods, resulting in poor accuracy and consistency of evaluation results and being unable to meet the requirements of modern equipment management for quantitative data. And the existing technologies only focus on the influence of a single factor (such as the failure rate of equipment or a single environmental condition) on the risk of equipment and facilities, lacking comprehensive consideration of the correlation and superposition effect among multiple factors. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for evaluating hidden danger factors of risk events to solve the problems of poor accuracy of evaluation results and strong limitations in risk assessment analysis existing in the evaluation of hidden danger factors of existing risk events of equipment and facilities.

[0006] According to one aspect of the present invention, a method for evaluating hidden danger factors of risk events is provided, including:

[0007] Obtaining the prior function type of the current safety hidden danger in the target risk event;

[0008] Determining each prior external factor of the current safety hidden danger and the market share of each prior external factor, and creating a target prior combination based on the target prior combination determination rule and each prior external factor;

[0009] Drawing a hidden danger factor evaluation curve based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor and the prior function type.

[0010] According to another aspect of the present invention, there is provided a hidden danger factor evaluation device for risk events, including:

[0011] A prior function type acquisition module, configured to acquire the prior function type of the current safety hidden danger in the target risk event;

[0012] A target prior combination creation module, configured to determine each prior external factor of the current safety hidden danger and the market share of each prior external factor, and create a target prior combination based on the target prior combination determination rule and each prior external factor;

[0013] A hidden danger factor evaluation curve drawing module, configured to draw a hidden danger factor evaluation curve based on the mean time to failure MTTF of the hidden danger, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

[0014] According to another aspect of the present invention, there is provided an electronic device, where the electronic device includes:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the hidden danger factor evaluation method for risk events according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the hidden danger factor evaluation method for risk events according to any embodiment of the present invention when executed.

[0019] In the technical solution of the embodiment of the present invention, by obtaining the prior function type of the current potential safety hazard in the target risk event, the prior external factors of the current potential safety hazard and the market share of each prior external factor are determined, and a target prior combination is created based on the target prior combination determination rule and each prior external factor. Furthermore, based on the hazard MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type, a hazard factor evaluation curve is drawn. This solution can analyze the impact of multiple prior external factors of the current potential safety hazard in the target risk event on the equipment and facilities, and through the hazard factor evaluation curve, display the correlation and superposition effect between each prior external factor, solving the problems of poor accuracy of the evaluation results and strong limitations in risk assessment analysis existing in the evaluation of potential safety hazard factors of the existing risk events of equipment and facilities, and being able to improve the accuracy of the evaluation results and the comprehensiveness of the analysis of the potential safety hazard factors of the risk event, meeting the needs of precise risk management.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a flowchart of a method for evaluating potential safety hazard factors of a risk event provided in Embodiment 1 of the present invention;

[0023] Figure 2 It is a flowchart of a method for evaluating potential safety hazard factors of a risk event provided in Embodiment 2 of the present invention;

[0024] Figure 3 It is a drawing logic diagram for drawing a hazard factor evaluation curve of prior external factors provided in Embodiment 2 of the present invention;

[0025] Figure 4 It is a schematic curve diagram of a hazard factor evaluation curve provided in Embodiment 2 of the present invention;

[0026] Figure 5 It is a structural schematic diagram of a device for evaluating potential safety hazard factors of a risk event provided in Embodiment 3 of the present invention;

[0027] Figure 6The schematic structural diagram of an electronic device that can be used to implement the embodiments of the present invention is shown. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be noted that the terms "current" and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 The flowchart of a method for evaluating potential factors of a risk event provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of comprehensively and accurately evaluating the risks of equipment and facilities. This method can be executed by a device for evaluating potential factors of a risk event, and this device for evaluating potential factors of a risk event can be implemented in the form of hardware and / or software, and this device for evaluating potential factors of a risk event can be configured in an electronic device. As Figure 1 shown, the method includes:

[0032] Step 110: Obtain the prior function type of the current safety hazard in the target risk event.

[0033] Among them, the target risk event may be a risk event that occurs during the operation of equipment and facilities. The target risk event may include, but is not limited to, compressor failure events and elevator failure events, etc. The current safety hazard may be the safety hazard for which prior factor evaluation is currently being performed in the target risk event. The prior function type may be the type of function for evaluating the prior factors of the safety hazard. Exemplarily, the prior function type may include, but is not limited to, Weibull function and exponential function, etc.

[0034] In an embodiment of the present invention, the current potential safety hazards of the target risk event may be first determined, and further, the type of the corresponding prior function for evaluating the prior factors of the current potential safety hazard is obtained, that is, the prior function type of the current potential safety hazard is obtained.

[0035] Step 120: Determine each prior external factor of the current potential safety hazard and the market share of each prior external factor, and create a target prior combination based on the target prior combination determination rule and each prior external factor.

[0036] Among them, the target risk event may include multiple potential safety hazards, and one potential safety hazard may correspond to multiple prior external factors. The target prior combination determination rule may be a prior external factor combination initially determined according to the evaluation level grade of the prior external factors. The target prior combination may be a prior external factor combination determined based on the target prior combination determination rule.

[0037] In an embodiment of the present invention, each prior external factor of the current potential safety hazard may be first determined, the market share of each prior external factor is obtained, and then, for each determined prior external factor, combination is performed according to the target prior combination determination rule to obtain a target prior combination.

[0038] Step 130: Draw a potential hazard factor evaluation curve based on the MTTF of the potential hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

[0039] Among them, the MTTF of the potential hazard may be the MTTF of the current potential safety hazard. The preset event MTTF of the target prior combination may be the MTTF of the corresponding combined event of the target prior combination. The preset value of the event correlation parameter may be a known parameter value set before calculating the preset event MTTF of the target prior combination. The potential hazard factor evaluation curve may be a curve describing the relevant information of the MTTF of the events corresponding to different prior factor combinations.

[0040] In an embodiment of the present invention, based on the MTTF of the potential hazard, the preset event MTTF of the target prior combination, the ratio of the preset value of the event correlation parameter, and the prior function type, the data required for drawing the initial potential hazard factor evaluation curve may be first determined, so that based on the data required for drawing the initial potential hazard factor evaluation curve and the market share of each prior external factor, the data required for deducing other potential hazard factor evaluation curves is determined, and then the full-scale potential hazard factor evaluation curve is drawn based on the determined data above.

[0041] The technical solution of the embodiment of the present invention obtains the prior function type of the current safety hazard in the target risk event, thereby determining the prior external factors of the current safety hazard and the market share of each prior external factor, and creates a target prior combination based on the target prior combination determination rule and each prior external factor, and then draws a hidden danger factor evaluation curve based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event association parameter, the market share of each prior external factor and the prior function type. This solution can analyze the impact of multiple prior external factors of the current safety hazard in the target risk event on equipment and facilities, and display the correlation and superposition effect between the prior external factors through the hidden danger factor evaluation curve, which solves the problems of poor accuracy of the evaluation results of the existing risk event hidden danger factor evaluation of equipment and facilities, and strong limitations of risk assessment analysis, and can improve the accuracy of the evaluation results of the risk event hidden danger factor evaluation and the comprehensiveness of the analysis, meeting the needs of precise risk management.

[0042] Embodiment 2

[0043] Figure 2 This is a flowchart of a method for evaluating the hidden danger factors of a risk event provided in Example 2 of the present invention. This embodiment is specific based on the above embodiment and provides a specific optional implementation method for obtaining the hidden danger MTTF before drawing the hidden danger factor evaluation curve based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event association parameter, the market share of each prior external factor and the prior function type. Figure 2 As shown, the method includes:

[0044] Step 210: Obtain the prior function type of the current safety hazard in the target risk event.

[0045] Step 220: determine each a priori external factor of the current safety hazard and the market share of each a priori external factor, and create a target a priori combination based on the target a priori combination determination rule and each a priori external factor.

[0046] In an optional embodiment of the present invention, the a priori external factors of the current safety hazards may include at least one of the equipment operating status, product quality, ambient temperature and humidity, installation environment, and installation process; the market share of the a priori external factors may include a first market share of the a priori external factors, and a second market share of the a priori external factors.

[0047] Among them, the first market share and the second market share are the market share of the better a priori external factors and the market share of the worse a priori external factors, respectively. The measurement standard of the good or bad a priori external factors can be set in advance. The better a priori external factors and the worse a priori external factors belong to two opposite evaluation levels of the a priori external factors.

[0048] In an alternative embodiment of the present invention, based on the target prior combination determination rule and each prior external factor, creating a target prior combination may include: determining a prior level full average combination and a non-average level single factor combination based on the target prior combination determination rule and the external factor levels of each prior external factor; generating a target prior combination according to the prior level full average combination and the non-average level single factor combination.

[0049] Among them, the external factor level may be an evaluation level describing the prior external factor. The external factor level may be one of better, average, or worse. The prior level weighted average combination may be a prior external factor combination composed of prior external factors at each average level. The factor non-average level single factor combination may be a prior external factor combination in which there is only one prior external factor at the average level.

[0050] In an embodiment of the present invention, the external factor levels of each prior external factor can be obtained, and then, according to the target prior combination determination rule, based on the external factor levels of each prior external factor, the prior external factors are combined to obtain a prior level full average combination and at least one non-average level single factor combination, so as to use the prior level full average combination and the non-average level single factor combination as the target prior combination.

[0051] Step 230, obtain the device historical operation data, environmental monitoring data, and industry database associated with the target risk event.

[0052] Among them, the device historical operation data may be the historical operation data of the device corresponding to the target risk event. The environmental monitoring data may be the monitoring data of the operating environment of the device corresponding to the target risk event. The industry database may be an industry-related information database of the device corresponding to the target risk event.

[0053] In an embodiment of the present invention, the device that has the target risk event can be determined, and then the device historical operation data, environmental monitoring data, and industry database of the determined device are obtained.

[0054] Step 240, send the device historical operation data, environmental monitoring data, and industry database associated with the target risk event to the expert review module, and obtain the hidden danger MTTF feedback by the expert review module.

[0055] Among them, the expert review module may be a module that sends data to an expert and obtains the expert feedback result.

[0056] In an embodiment of the present invention, the device historical operation data, environmental monitoring data, and industry database associated with the target risk event can be sent to the expert review module to set the hidden danger MTTF for the current safety hidden danger of the target risk event by an expert.

[0057] Step 250: Draw a hidden danger factor evaluation curve based on the MTTF of the hidden danger, the MTTF of the preset event of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

[0058] In an alternative embodiment of the present invention, drawing a hidden danger factor evaluation curve based on the MTTF of the hidden danger, the MTTF of the preset event of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type may include: determining the hidden danger factor evaluation curve of the target prior combination according to the MTTF of the hidden danger, the MTTF of the preset event of the target prior combination, the preset value of the event correlation parameter, and the prior function type; determining the hidden danger factor evaluation curve of the non-target prior combination according to the hidden danger factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter.

[0059] Among them, the non-target prior combination may be other combinations in the full combination of each prior external factor except the target prior combination.

[0060] In the embodiment of the present invention, the actual MTTF and related plotting parameters of the event corresponding to the target prior combination may be determined according to the MTTF of the hidden danger, the MTTF of the preset event of the target prior combination, the preset value of the event correlation parameter, and the prior function type. Then, based on the actual MTTF and related plotting parameters of the event corresponding to the target prior combination, the hidden danger factor evaluation curve of the target prior combination is drawn. Thus, based on the relevant data of the hidden danger factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter, the actual MTTF and related plotting parameters of the event corresponding to the non-target prior combination are determined. Thus, based on the actual MTTF and related plotting parameters of the event corresponding to the non-target prior combination, the hidden danger factor evaluation curve of the non-target prior combination is drawn. Among them, the actual MTTF can be used to reflect the actual MTTF of the prior external factor combination corresponding to the event.

[0061] In an alternative embodiment of the present invention, determining the risk factor evaluation curve of the target prior combination according to the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type may include: when the preset event MTTF of the target prior combination is less than the hidden danger MTTF, using the preset event MTTF of the target prior combination as the actual MTTF of the target prior combination; when the preset event MTTF of the target prior combination is greater than the hidden danger MTTF, determining the actual MTTF of the target prior combination based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type; and determining the risk factor evaluation curve of the target prior combination according to the actual MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type.

[0062] In an embodiment of the present invention, the preset event MTTF of the current target prior combination can be compared with the hidden danger MTTF. If the preset event MTTF of the current target prior combination is less than the hidden danger MTTF, then the preset event MTTF of the current target prior combination is used as the actual MTTF of the current target prior combination. If the preset event MTTF of the target prior combination is greater than the hidden danger MTTF, then based on the hidden danger MTTF, the preset event MTTF of the target prior combination, and the preset value of the event correlation parameter, substitute them into the relevant plotting parameter calculation formula corresponding to the prior function type to obtain the relevant plotting parameters. Furthermore, based on the actual MTTF of the event corresponding to the target prior combination and the relevant plotting parameters, draw the risk factor evaluation curve of the target prior combination.

[0063] In an alternative embodiment of the present invention, determining the risk factor evaluation curve of the non-target prior combination according to the risk factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter may include: determining the risk factor evaluation curve of the first recursive prior combination in the non-target prior combination according to the actual MTTF corresponding to the risk factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter; and determining the risk factor evaluation curve of the second recursive prior combination in the non-target prior combination according to the risk factor evaluation curve of the first recursive prior combination, the risk factor evaluation curve of the target prior combination, and the preset value of the event correlation parameter.

[0064] Among them, the first recursive prior combination may be a combination obtained by replacing the non-average-level factor in the non-average-level single-factor combination with an external factor at the opposite level. The second recursive prior combination may be other combinations in the full combination of prior external factors except for the target prior combination and the first recursive prior combination.

[0065] In an embodiment of the present invention, the actual MTTF corresponding to the hidden factor evaluation curve of the prior level full average combination in the target prior combination and the actual MTTF corresponding to the hidden factor evaluation curve of the non-average level single factor combination can be obtained first. Then, through the weighted summation method and the market share of the non-average level factors in the non-average level single factor combination, the actual MTTF of the first recursive prior combination in the non-target prior combination is calculated, and based on the preset values of the event correlation parameters corresponding to the prior level full average combination and the non-average level single factor combination, the drawing correlation parameters of the first recursive prior combination are calculated (the specific calculation method can be set by oneself). Thus, according to the actual MTTF and the drawing correlation parameters of the first recursive prior combination, the hidden factor evaluation curve of the first recursive prior combination in the non-target prior combination is drawn.

[0066] After obtaining the hidden factor evaluation curve of the first recursive prior combination, the change rates of the external factor levels of the prior external factors from average to better and from average to worse, as well as the change rates of the corresponding preset values of the event correlation parameters, can be calculated according to the actual MTTF corresponding to the hidden factor evaluation curve of the first recursive prior combination, the actual MTTF corresponding to the hidden factor evaluation curve of the prior level full average combination in the target prior combination, and the preset values of the event correlation parameters. Thus, based on the calculated change rates, the actual MTTF and the drawing correlation parameters of the second recursive prior combination in the non-target prior combination are determined. Then, according to the actual MTTF and the drawing correlation parameters of the second recursive prior combination, the hidden factor evaluation curve of the second recursive prior combination in the non-target prior combination is drawn.

[0067] In a specific example, taking three prior external factors A, B, and C as an example, each prior external factor includes three external factor levels: better, worse, and average. By arranging and combining them, 27 prior external factor combinations can be obtained. Assume that A1 / B1 / C1 represents that the prior external factors A / B / C are all at the better level, and A3 / B3 / C3 represents that the prior external factors A / B / C are all at the average level. The drawing logic diagram of the hidden factor evaluation curve of the prior external factors is as Figure 3 shown. First, determine the target risk event and its hidden hazards, then determine the type of prior function corresponding to the current safety hazard, and obtain the hidden hazard MTTF, each prior external factor, and the better / worse market share of each prior external factor. Then, arrange and combine each prior external factor to obtain the full combination of each prior external factor. Assign values to the MTTF and β of A3 / B3 / C3, A1 / B3 / C3, A3 / B1 / C3, and A3 / B3 / C1, and further calculate to generate all the hidden factor evaluation curves.

[0068] All prior external factor combinations have a common hidden danger MTTF, and each prior external factor combination has a separate preset event MTTF. The hidden danger MTTF is used to represent the statistical expected value of the first failure time of a device or system under specific operating environments and usage conditions. The preset event MTTF is used to represent the statistical expected value of the first failure time of this type of event based on the average levels of prior external factors A / B / C. When the preset event MTTF is greater than the hidden danger MTTF, the preset event MTTF is actually a value affected by the hidden danger MTTF and is not the actual MTTF. For example, if the hidden danger MTTF is 20 years and the preset event MTTF is 50 years, this 50 years is actually affected by the replacement of the device itself with an average failure once every 20 years. For this event, it fails on average every 50 years. In fact, the MTTF of this event is between 20 years and 50 years. Therefore, the preset event MTTF needs to be transformed into the actual MTTF through a formula.

[0069] The detailed calculation process of the evaluation curves for all hidden danger factors is as follows:

[0070] First, based on the device's historical operation data, environmental monitoring data, and industry databases, combined with expert experience, assign values to the hidden danger MTTF, the market share of different levels of each prior external factor, the preset event MTTF and β value (preset value of the event correlation parameter) for A3 / B3 / C3, the event MTTF and β value for A1 / B3 / C3, the event MTTF and β value for A3 / B1 / C3, and the event MTTF and β value for A3 / B3 / C1. According to the parameter information of these four target prior combinations, the parameter information of the remaining 23 non-target prior combinations can be obtained.

[0071] It should be noted that the rule for determining the target prior combination is to determine the full average level (A3 / B3 / C3) and combinations with one factor at a better level (A1 / B3 / C3), (A3 / B1 / C3), (A3 / B3 / C1), and assign values to the preset event MTTF of the target prior combination. And so on, the target prior combinations determined by two prior external factors are A3 / B3, A1 / B3, and A3 / B1.

[0072] Suppose there are 3 prior external factors. The determination logic of the hidden danger factor evaluation curve is as follows:

[0073] Based on the assigned values of the hidden danger MTTF, preset event MTTF, and β, calculate the α values of the four basic curves (the evaluation curves of the hidden danger factors of the target prior combinations are the basic curves). If the preset event MTTF is less than the hidden danger MTTF, then the actual MTTF is the preset event MTTF, and the prior function type is the Weibull function. At this time If the preset event MTTF is greater than the hidden danger MTTF, the α values of the four basic curves are calculated according to the following formula, and then the actual MTTF corresponding to the four basic curves is obtained.

[0074]

[0075] Based on the known actual MTTF of A3 / B3 / C3, the actual MTTF of A1 / B3 / C3, and the market shares of A1 and A2, the MTTF of A2 / B3 / C3 is calculated by weighted summation of MTTF, and so on to calculate the MTTF of A3 / B2 / C3 and the MTTF of A3 / B3 / C2. Exemplarily, the product value of the actual MTTF of A1 / B3 / C3 and the market share of A1 can be calculated, and then the difference between the actual MTTF of A3 / B3 / C3 and this product value is calculated, and the quotient of this difference and the market share of A2 is used as the MTTF of A2 / B3 / C3.

[0076] Since the β value of A3 / B3 / C3 and the β value of A1 / B3 / C3 are known, the β value of A2 / B3 / C3 = min{β(A3 / B3 / C3), β(A1 / B3C3)} - P(A1). Similarly, the β values of A3 / B2 / C3 and A3 / B3 / C2 can be calculated. β(A3 / B3 / C3) represents the β value of A3 / B3 / C3, and β(A1 / B3 / C3) represents the β value of A1 / B3 / C3. P(A1) represents the market share of A1.

[0077] Based on the known actual MTTF and β value of A3 / B3 / C3, the actual MTTF and β value of A1 / B3 / C3, and the actual MTTF and β value of A2 / B3 / C3, the MTTF change rate and β change rate of A3->A1 and A3->A2 can be calculated. Similarly, the change rates of B3->B1, B3->B2, C3->C1, and C3->C2 can be calculated. Therefore, based on the actual MTTF and β value of A3 / B3 / C3, the actual MTTF and β corresponding to any hidden danger factor evaluation curve can be obtained. For example, the actual MTTF of A2 / B2 / C2 is calculated based on the following formula:

[0078] The actual MTTF of A2 / B2 / C2 = (the actual MTTF of A3 / B3 / C3) * (the actual MTTF change rate of A3->A2) * (the MTTF change rate of B3->B2) * (the MTTF change rate of C3->C2).

[0079] Optionally, the risk factor assessment curves (a total of 27) can be presented in the visualization module, including 4 risk factor assessment curves for the target prior combinations and 23 risk factor assessment curves for the non-target prior combinations. The corresponding prior factor combinations, the determined actual MTTF (equivalent to the average MTTF of the events corresponding to the prior factor combinations), the α value, and the β value are recorded in the risk factor assessment curves. Assuming that the prior function type is the Weibull function, a risk factor assessment curve when the average MTTF is 6.48 years, the α value is 2575.527270, the β value is 5.0, and the prior factors are good air quality, good material, and good product quality can be seen in Figure 4 .

[0080] At this time, the parameter values of all 27 prior external factor combinations are obtained, and the distribution function curve can be drawn accordingly.

[0081] This solution solves the defect of the fixed MTTF value and determines the multi-dimensional MTTF values (including the average MTTF, the better MTTF, and the worse MTTF. The average MTTF represents the MTTF of the prior level full-average combination, the better MTTF represents the MTTF of the external factor combination that only includes one better external factor, and the worse MTTF represents the MTTF of the external factor combination that only includes one worse external factor). Compared with the limitation of the fixed MTTF value in the traditional method, it can accurately reflect the impact of environmental changes on the equipment life, thereby providing more accurate and dynamic basic data support for risk assessment.

[0082] Combining the historical operation data of the equipment, the environmental monitoring data, and the industry database with expert experience to determine the hidden danger MTTF significantly reduces the assignment deviation caused by subjectivity in the traditional risk assessment method, improves the scientificity and consistency of the assessment results, and forms a multi-dimensional prior factor library based on the operation scenarios of the equipment and facilities, effectively making up for the defects of single factor selection or insufficient coverage in the existing technology, thereby realizing the comprehensiveness and accuracy of the risk assessment scope.

[0083] In the technical solution of the embodiment of the present invention, by obtaining the prior function type of the current potential safety hazard in the target risk event, the prior external factors of the current potential safety hazard and the market share of each prior external factor are determined, and a target prior combination is created based on the target prior combination determination rule and each prior external factor. Furthermore, the historical operation data of the equipment, environmental monitoring data, and industry database associated with the target risk event are obtained. Further, the historical operation data of the equipment, environmental monitoring data, and industry database associated with the target risk event are sent to the expert review module, and the potential hazard MTTF feedback by the expert review module is obtained. And based on the potential hazard MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type, a potential hazard factor evaluation curve is drawn. This solution can analyze the impact of multiple prior external factors of the current potential safety hazard in the target risk event on the equipment and facilities, and through the potential hazard factor evaluation curve, display the correlation and superposition effect between each prior external factor, solving the problems of poor accuracy of the evaluation result and strong limitation of the risk assessment analysis existing in the evaluation of the potential hazard factors of the existing risk events of the equipment and facilities, and being able to improve the accuracy of the evaluation result of the potential hazard factors of the risk event and the comprehensiveness of the analysis, meeting the requirements of precise risk management.

[0084] Embodiment III

[0085] Figure 5 It is a schematic structural diagram of a potential hazard factor evaluation device for a risk event provided by Embodiment III of the present invention. As Figure 5 shown, the device includes:

[0086] A prior function type acquisition module 310, configured to acquire the prior function type of the current potential safety hazard in the target risk event;

[0087] A target prior combination creation module 320, configured to determine the prior external factors of the current potential safety hazard and the market share of each prior external factor, and create a target prior combination based on the target prior combination determination rule and each prior external factor;

[0088] A potential hazard factor evaluation curve drawing module 330, configured to draw a potential hazard factor evaluation curve based on the mean time to failure MTTF of the potential hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

[0089] The technical solution of the embodiment of the present invention determines each prior external factor of the current potential safety hazard and the market share of each prior external factor by obtaining the prior function type of the current potential safety hazard in the target risk event, creates a target prior combination based on the target prior combination determination rule and each prior external factor, and then draws a potential hazard factor evaluation curve based on the MTTF of the potential hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type. This solution can analyze the impact of multiple prior external factors of the current potential safety hazard in the target risk event on equipment and facilities, and display the correlation and superposition effect between each prior external factor through the potential hazard factor evaluation curve, solving the problems of poor accuracy of the evaluation results and strong limitations in risk assessment analysis existing in the evaluation of potential hazard factors of existing risk events of equipment and facilities, and being able to improve the accuracy of the evaluation results and the comprehensiveness of the analysis of the evaluation of potential hazard factors of risk events, meeting the needs of precise risk management.

[0090] Optionally, the prior external factors of the current potential safety hazard include at least one of the equipment operation status, product quality, environmental temperature and humidity, installation environment, and installation process; the market share of the prior external factor includes the first market share of the prior external factor and the second market share of the prior external factor.

[0091] Optionally, the target prior combination creation module 320 is specifically configured to determine a prior level full average combination and a factor non-average level single factor combination based on the target prior combination determination rule and the external factor levels of each prior external factor; generate a target prior combination according to the prior level full average combination and the factor non-average level single factor combination.

[0092] Optionally, the potential hazard factor evaluation device of the risk event includes a potential hazard MTTF acquisition module, configured to acquire the device historical operation data, environmental monitoring data, and industry database associated with the target risk event; send the device historical operation data, environmental monitoring data, and industry database associated with the target risk event to the expert review module, and acquire the potential hazard MTTF fed back by the expert review module.

[0093] Optionally, the potential hazard factor evaluation curve drawing module 330 is specifically configured to determine the potential hazard factor evaluation curve of the target prior combination according to the potential hazard MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type; determine the potential hazard factor evaluation curve of the non-target prior combination according to the potential hazard factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter.

[0094] Optionally, the hidden danger factor evaluation curve drawing module 330 is specifically configured to use the preset event MTTF of the target prior combination as the actual MTTF of the target prior combination when the preset event MTTF of the target prior combination is less than the hidden danger MTTF; when the preset event MTTF of the target prior combination is greater than the hidden danger MTTF, determine the actual MTTF of the target prior combination based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type; and determine the hidden danger factor evaluation curve of the target prior combination according to the actual MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type.

[0095] Optionally, the hidden danger factor evaluation curve drawing module 330 is specifically configured to determine the hidden danger factor evaluation curve of the first recursive prior combination in the non-target prior combination according to the actual MTTF corresponding to the hidden danger factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter; and determine the hidden danger factor evaluation curve of the second recursive prior combination in the non-target prior combination according to the hidden danger factor evaluation curve of the first recursive prior combination, the hidden danger factor evaluation curve of the target prior combination, and the preset value of the event correlation parameter.

[0096] The hidden danger factor evaluation device for risk events provided by the embodiments of the present invention can execute the hidden danger factor evaluation method for risk events provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0097] Embodiment 4

[0098] Figure 6 The structural schematic diagram of the electronic device that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0099] As Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0101] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the risk event hazard factor assessment method.

[0102] In some embodiments, the risk event hazard factor assessment method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the risk event hazard factor assessment method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the risk event hazard factor assessment method in any other appropriate manner (e.g., by means of firmware).

[0103] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0105] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, voice input, or tactile input).

[0107] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0108] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of traditional physical hosts and VPS servers, such as difficult management and weak business scalability.

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0110] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating potential factors of risk events, characterized in that, Including: Obtain the prior function type of the current safety hazard in the target risk event; Determine each prior external factor of the current safety hazard and the market share of each prior external factor, and create a target prior combination based on the target prior combination determination rule and each prior external factor; Draw a hazard factor evaluation curve based on the mean time to failure (MTTF) of the hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

2. The method according to claim 1, wherein The prior external factors of the current safety hazard include at least one of the equipment operating state, product quality, environmental temperature and humidity, installation environment, and installation process; The market share of the prior external factor includes the first market share of the prior external factor and the second market share of the prior external factor.

3. The method according to claim 1, wherein Creating a target prior combination based on the target prior combination determination rule and each prior external factor includes: Determine the prior level full average combination and the non-average level single factor combination based on the target prior combination determination rule and the external factor levels of each prior external factor; Generate a target prior combination according to the prior level full average combination and the non-average level single factor combination.

4. The method according to claim 1, wherein Before drawing a hazard factor evaluation curve based on the MTTF of the hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type, it further includes: Obtain the device historical operation data, environmental monitoring data, and industry database associated with the target risk event; Send the device historical operation data, environmental monitoring data, and industry database associated with the target risk event to the expert review module, and obtain the MTTF of the hazard feedback by the expert review module.

5. The method according to claim 1, wherein Drawing a hazard factor evaluation curve based on the MTTF of the hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type includes: Determine the hazard factor evaluation curve of the target prior combination according to the MTTF of the hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type; Determine the hazard factor evaluation curve of the non-target prior combination according to the hazard factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter.

6. The method according to claim 5, characterized in that, Determining the hazard factor evaluation curve of the target prior combination according to the MTTF of the hazard, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type includes: When the preset event MTTF of the target prior combination is less than the MTTF of the hazard, use the preset event MTTF of the target prior combination as the actual MTTF of the target prior combination; When the preset event MTTF of the target prior combination is greater than the hidden danger MTTF, based on the hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type, determine the actual MTTF of the target prior combination; According to the actual MTTF of the target prior combination, the preset value of the event correlation parameter, and the prior function type, determine the hidden danger factor evaluation curve of the target prior combination.

7. The method according to claim 6, wherein According to the hidden danger factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter, determine the hidden danger factor evaluation curve of the non-target prior combination, including: According to the actual MTTF corresponding to the hidden danger factor evaluation curve of the target prior combination, the market share of each prior external factor, and the preset value of the event correlation parameter, determine the hidden danger factor evaluation curve of the first recursive prior combination in the non-target prior combination; According to the hidden danger factor evaluation curve of the first recursive prior combination, the hidden danger factor evaluation curve of the target prior combination, and the preset value of the event correlation parameter, determine the hidden danger factor evaluation curve of the second recursive prior combination in the non-target prior combination.

8. A hidden danger factor evaluation device for risk events, characterized in that, Including: A prior function type acquisition module, configured to acquire the prior function type of the current hidden danger in the target risk event; A target prior combination creation module, configured to determine each prior external factor of the current hidden danger and the market share of each prior external factor, and create a target prior combination based on the target prior combination determination rule and each prior external factor; A hidden danger factor evaluation curve drawing module, configured to draw a hidden danger factor evaluation curve based on the mean time to failure of hidden danger MTTF, the preset event MTTF of the target prior combination, the preset value of the event correlation parameter, the market share of each prior external factor, and the prior function type.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for evaluating hidden danger factors of the risk event according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to execute the method for evaluating hidden danger factors of the risk event according to any one of claims 1-7 when executed.

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